{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:11:19Z","timestamp":1776888679916,"version":"3.51.2"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Representing high-volume and high-order data is an essential problem, especially in machine learning field. Although existing two-dimensional (2D) discriminant analysis achieves promising performance, the single and linear projection features make it difficult to analyze more complex data. In this paper, we propose a novel convolutional two-dimensional linear discriminant analysis (2D LDA) method for data representation. In order to deal with nonlinear data, a specially designed Convolutional Neural Networks (CNN) is presented, which can be proved having the equivalent objective function with common 2D LDA. In this way, the discriminant ability can benefit from not only the nonlinearity of Convolutional Neural Networks, but also the powerful learning process. Experiment results on several datasets show that the proposed method performs better than other state-of-the-art methods in terms of classification accuracy.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/408","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"2929-2935","source":"Crossref","is-referenced-by-count":17,"title":["Convolutional 2D LDA for Nonlinear Dimensionality Reduction"],"prefix":"10.24963","author":[{"given":"Qi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University"},{"name":"Unmanned System Research Institute (USRI), Northwestern Polytechnical University"}]},{"given":"Zequn","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University"}]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, P. R. China"}]},{"given":"Yuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:53:47Z","timestamp":1501242827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/408"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/408","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}